
"""simple docstring"""






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType



class 			lowercase					:






			def __init__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=2      ,SCREAMING_SNAKE_CASE_  :	List[Any]=3      ,SCREAMING_SNAKE_CASE_  :	str=64      ,SCREAMING_SNAKE_CASE_  :	List[Any]=None			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  np.random.default_rng(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  length
				lowerCAmelCase												=  rng.normal(size=(length,)			).astype(np.floataa			)
				lowerCAmelCase												=  a * self.x + b + rng.normal(scale=0.1      ,size=(length,)			).astype(np.floataa			)






			def __len__(self  :	int			)       ->	Optional[Any]:





				"""simple docstring"""


				return self.length






			def __getitem__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	str			)       ->	str:





				"""simple docstring"""


				return {"x": self.x[i], "y": self.y[i]}





class 			lowercase					( torch.nn.Module		):






			def __init__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Any=0      ,SCREAMING_SNAKE_CASE_  :	Any=0      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=False			)       ->	str:





				"""simple docstring"""


				super().__init__()
				lowerCAmelCase												=  torch.nn.Parameter(torch.tensor([2, 3]			).float()			)
				lowerCAmelCase												=  torch.nn.Parameter(torch.tensor([2, 3]			).float()			)
				lowerCAmelCase												=  True






			def 					UpperCAmelCase							(self  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	Any=None			)       ->	Optional[int]:





				"""simple docstring"""


				if self.first_batch:
					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""			)
					lowerCAmelCase												=  False
				return x * self.a[0] + self.b[0]





class 			lowercase					( torch.nn.Module		):






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int=0      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=0      ,SCREAMING_SNAKE_CASE_  :	Dict=False			)       ->	Tuple:





				"""simple docstring"""


				super().__init__()
				lowerCAmelCase												=  torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_			).float()			)
				lowerCAmelCase												=  torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_			).float()			)
				lowerCAmelCase												=  True






			def 					UpperCAmelCase							(self  :	str      ,SCREAMING_SNAKE_CASE_  :	int=None			)       ->	Optional[int]:





				"""simple docstring"""


				if self.first_batch:
					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""			)
					lowerCAmelCase												=  False
				return x * self.a + self.b





def __magic_name__	(				_lowerCamelCase:		Dict,					_lowerCamelCase:		int = 16			)					->						List[str]:

	'''simple docstring'''




	from datasets import load_dataset
	from transformers import AutoTokenizer

	lowerCAmelCase												=  AutoTokenizer.from_pretrained('''bert-base-cased'''			)
	lowerCAmelCase												=  {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
	lowerCAmelCase												=  load_dataset('''csv''',					data_files=_lowerCamelCase			)
	lowerCAmelCase												=  datasets['''train'''].unique('''label'''			)

	lowerCAmelCase												=  {v: i for i, v in enumerate(_lowerCamelCase			)}

	def tokenize_function(_lowerCamelCase:		List[str]			):
		# max_length=None => use the model max length (it's actually the default)
		lowerCAmelCase												=  tokenizer(
		    examples['''sentence1'''],					examples['''sentence2'''],					truncation=_lowerCamelCase,					max_length=_lowerCamelCase,					padding='''max_length'''			)
		if "label" in examples:
			lowerCAmelCase												=  [label_to_id[l] for l in examples['''label''']]
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	lowerCAmelCase												=  datasets.map(
	    _lowerCamelCase,					batched=_lowerCamelCase,					remove_columns=['''sentence1''', '''sentence2''', '''label'''],					)

	def collate_fn(_lowerCamelCase:		Tuple			):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_lowerCamelCase,					padding='''max_length''',					max_length=128,					return_tensors='''pt'''			)
		return tokenizer.pad(_lowerCamelCase,					padding='''longest''',					return_tensors='''pt'''			)

	# Instantiate dataloaders.
	lowerCAmelCase												=  DataLoader(tokenized_datasets['''train'''],					shuffle=_lowerCamelCase,					collate_fn=_lowerCamelCase,					batch_size=2			)
	lowerCAmelCase												=  DataLoader(tokenized_datasets['''validation'''],					shuffle=_lowerCamelCase,					collate_fn=_lowerCamelCase,					batch_size=1			)

	return train_dataloader, eval_dataloader




"""simple docstring"""






from ..utils import DummyObject, requires_backends



class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	str:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[int]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	int      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	int      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	List[str]      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	Dict:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Optional[int]      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Dict      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	List[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Tuple      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Optional[Any]      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)


